Our earlier basic studies have shown that MEG recordings during cognitive tasks have the ability to localize brain activity in comparable fashion to functional MRI. Specifically reductions in of power in the beta frequency band at the cortical level has been found to agree with BOLD activation results. However, electrophysiological recordings such as the MEG/EEG have fine grained temporal information not possible with other imaging techniques. Further studies have demonstrated the ability to localize signals in deeper structures such as the amygdala and to investigate the relation of visual awareness to gamma band signals. We have also found that GABA (gamma-aminobutyric acid) concentration in the anterior cingulate cortex correlates with spatially localized resting MEG beta band power. We now have a much larger cohort of subjects that we hope that with further investigation will reveal additional relationships between gamma power and GABA systems and that will also show difference across patient groups. Differences in the degree of activation especially in frontal regions as indexed by beta desynchronization during a working memory task have been found between patients with schizophrenia compared to well siblings and normal control volunteers. Previously we have seen that this activation reveals an interaction with genotype for the well studied COMT marker.. With a well matched set of patients, siblings and controlsfor working memory task performance patients show a distinct reduced DLPFC activation in apparent distinction to increased BOLD relative to task load. The MEG analysis isolates a working memory component that may reflect a different aspect of cortical processing. We are extending these results to examine difference in gamma band across the subject groups. The relation of the different frequency bands to patterns of blood flow activation we hope will reveal more specific targets of for the neurophysiology underlying patient differences. Differences in network patterns and dynamics are key to understanding underlying pathology in clinical groups. Previously Bassett et al have shown that functional network variations in patient groups can be related to behavioral outcomes on cognitive activities. Even at rest patients with schizophrenia have gamma power reduction compared to normal subjects.. We have found distinct patterns of the temporal sequence of brain regions involved in these memory tasks that show a variety of individual differences across subjects. This has been extended using graph theoretic measures as a way to capture properties of the pattern activity across brain regions. Sienenhuhner et al have now done an extensive analysis of functional connectivity patterns during working memory in a small group of patients with schizophrenia and healthy controls. They have found distinct changes in the topology of these networks both in complexity and in the relation of cross-frequency interactions. We plan to follow this up with a larger number of subjects and to compare resting as well as task related networks. Previous work examined whether reorganization of functional brain networks can be seen in response to cognitive remediation strategies using auditory task training in both patients and healthy. We were able to show significant changes in power and coherence in brain activity that were associated with improved behavioral performance. This biomarker would allow tracking the outcome of remediation strategies targeting specific cognitive deficits in neuropsychiatric disorders. MEG is able to show wide spread critical dynamics that are crucial to optimize information processing in such networks. These optimal dynamics may be essential for the plasticity necessary for appropriate adaptive behavior. New strategies for tracking and enhancing this plasticity are planned.